Self-Driving Car Engineer Nanodegree¶
Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
Camera Calibration¶
In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
#####################################################################################
def camera_calibration(nx=9, ny=6):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((nx*ny,3), np.float32)
objp[:,:2] = np.mgrid[0:nx,0:ny].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('camera_cal/calibration*.jpg')
mx=5 #4
my=4 #5
fig, axes = plt.subplots(mx, my, figsize=(40, 30))
fig.tight_layout()
im=0
# Step through the list and search for chessboard corners
for fname in images:
#img = cv2.imread(fname)
#gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img = mpimg.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx,ny),None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (nx,ny), corners, ret)
axes[im%mx,im//mx].set_title(fname, fontsize=30)
axes[im%mx,im//mx].imshow(img)
else:
axes[im%mx,im//mx].set_title("False:"+fname, fontsize=30)
axes[im%mx,im//mx].imshow(img)
im = im+1
return cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
#####################################################################################
ret, mtx, dist, rvecs, tvecs = camera_calibration(9,6)
In [2]:
#####################################################################################
def img_undistort(img, mtx=None, dist=None):
return cv2.undistort(img, mtx, dist, None, mtx)
#####################################################################################
def test_img_undistort(img, mtx=None, dist=None):
undist=img_undistort(img, mtx, dist)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(undist)
ax2.set_title('Undistorted Image', fontsize=30)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
#####################################################################################
img = mpimg.imread('camera_cal/calibration5.jpg')
test_img_undistort(img, mtx, dist)
img = mpimg.imread('test_images/test5.jpg')
test_img_undistort(img, mtx, dist)
2. Color/Gradient Threshold¶
In [3]:
#####################################################################################
# Define a function that takes an image, gradient orientation,
# and threshold min / max values.
def abs_sobel_thresh(gray, orient='x', sobel_kernel=3, thresh=(0, 255)):
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
# Sobel x
#sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Take the derivative in x
#abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
#scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold x gradient
#sxbinary = np.zeros_like(scaled_sobel)
#sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
# Return the result
return binary_output
# Define a function to return the magnitude of the gradient
# for a given sobel kernel size and threshold values
def mag_thresh(gray, sobel_kernel=3, mag_thresh=(0, 255)):
# Take both Sobel x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the gradient magnitude
gradmag = np.sqrt(sobelx**2 + sobely**2)
# Rescale to 8 bit
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# Create a binary image of ones where threshold is met, zeros otherwise
binary_output = np.zeros_like(gradmag)
binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
# Return the binary image
return binary_output
# Define a function to threshold an image for a given range and Sobel kernel
def dir_threshold(gray, sobel_kernel=3, thresh=(0, np.pi/2)):
# gray = img
# Calculate the x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the gradient direction,
# apply a threshold, and create a binary image result
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
# Return the binary image
return binary_output
def orient_threshold(gray, orient="mag",sobel_kernel=3, thresh=(0, 255)) :
if (orient == 'x' or orient == 'y') :
return abs_sobel_thresh(gray, orient, sobel_kernel, thresh)
elif (orient == 'mag'):
return mag_thresh(gray, sobel_kernel, thresh)
else:
return dir_threshold(gray, sobel_kernel, thresh)
#####################################################################################
def test_thresholds(img):
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Choose a Sobel kernel size
ksize = 3 # Choose a larger odd number to smooth gradient measurements
# Apply each of the thresholding functions
gradx = orient_threshold(gray, orient='x', sobel_kernel=ksize, thresh=(20, 100))
grady = orient_threshold(gray, orient='y', sobel_kernel=ksize, thresh=(20, 100))
mag_binary = orient_threshold(gray, orient='mag', sobel_kernel=ksize, thresh=(20, 100))
dir_binary = orient_threshold(gray, orient='dir', sobel_kernel=ksize, thresh=(0.7, 1.3))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
fig, axes = plt.subplots(2, 3, sharex=True, sharey=True, figsize=(24, 9))
axes[0,0].set_title('Original Image', fontsize=30)
axes[0,0].imshow(img)
axes[0,1].set_title('Gradient-X Image', fontsize=30)
axes[0,1].imshow(gradx, cmap='gray')
axes[0,2].set_title('Gradient-Y Image', fontsize=30)
axes[0,2].imshow(grady, cmap='gray')
axes[1,0].set_title('Magnitude Image', fontsize=30)
axes[1,0].imshow(mag_binary, cmap='gray')
axes[1,1].set_title('Direction Image', fontsize=30)
axes[1,1].imshow(dir_binary, cmap='gray')
axes[1,2].set_title('Combined Image', fontsize=30)
axes[1,2].imshow(combined, cmap='gray')
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
#####################################################################################
img = mpimg.imread('test_images/test5.jpg')
test_thresholds(img)
In [4]:
def mask_white_yellow(img):
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
# white color region
lower = np.uint8([ 0, 200, 0])
upper = np.uint8([255, 255, 255])
white_mask = cv2.inRange(hls, lower, upper)
# yellow color region
lower = np.uint8([ 10, 0, 100])
upper = np.uint8([ 40, 255, 255])
yellow_mask = cv2.inRange(hls, lower, upper)
# combine the mask
mask = cv2.bitwise_or(white_mask, yellow_mask)
return cv2.bitwise_and(img, img, mask = mask)
In [5]:
def hls_threshold(img, s_thresh=(170, 255), orient='x', sx_thresh=(20, 100)):
# Mask the white and yellow colors
yw_img=mask_white_yellow(img)
#Convert the image to grayscale
gray = cv2.cvtColor(yw_img, cv2.COLOR_RGB2GRAY)
#gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Convert to HLS color space and separate the V channel
hls = cv2.cvtColor(yw_img, cv2.COLOR_RGB2HLS).astype(np.float)
#l_channel = hls[:,:,1]
s_channel = hls[:,:,2]
# Threshold color channel
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
combined = np.zeros_like(s_channel)
# Threshold x gradient
ksize=3
if (orient != 'none') :
sxbinary = orient_threshold(gray, orient, sobel_kernel=ksize, thresh=sx_thresh)
#sxbinary = orient_threshold(l_channel, orient, sobel_kernel=ksize, thresh=sx_thresh)
combined[((s_binary == 1)) | (sxbinary == 1)] = 1
else:
combined[(s_binary == 1)] = 1
color_binary = combined
return color_binary
#####################################################################################
def test_hls_threshold(img):
result = hls_threshold(img, s_thresh=(5, 255), orient="x", sx_thresh=(20, 100))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(result, cmap='gray')
ax2.set_title('S-Channel & Gradient-X Image', fontsize=30)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
#####################################################################################
img = mpimg.imread('test_images/straight_lines1.jpg')
test_hls_threshold(img)
img = mpimg.imread('test_images/test5.jpg')
test_hls_threshold(img)
img = mpimg.imread('tmp-1.jpg')
test_hls_threshold(img)
3. Perspective Transform¶
In [6]:
# Define calibration box in source(original) and destination (desired or warped)
img_size = (1280,720)
src = np.float32(
[[(img_size[0] / 2) - 55, img_size[1] / 2 + 100],
[((img_size[0] / 6) - 10), img_size[1]],
[(img_size[0] * 5 / 6) + 60, img_size[1]],
[(img_size[0] / 2 + 55), img_size[1] / 2 + 100]])
dst = np.float32(
[[(img_size[0] / 4), 0],
[(img_size[0] / 4), img_size[1]],
[(img_size[0] * 3 / 4), img_size[1]],
[(img_size[0] * 3 / 4), 0]])
print(src)
print(dst)
# Compute the perspective transform, M
M = cv2.getPerspectiveTransform(src, dst)
# Compute the inverse perspective transfrom Minv
Minv = cv2.getPerspectiveTransform(dst, src)
##############################################################################
# Define perspective tranform function
def warper(img):
img_size = (img.shape[1], img.shape[0])
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
return warped
##############################################################################
def test_warper(img):
result = warper(img)
undist=img_undistort(img, mtx, dist)
result = warper(undist)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=40)
x = [src[0][0],src[1][0],src[2][0],src[3][0],src[0][0]]
y = [src[0][1],src[1][1],src[2][1],src[3][1],src[0][1]]
ax1.plot(x, y, color='red', alpha=0.4, linewidth=3, solid_capstyle='round', zorder=2)
ax2.imshow(result, cmap='gray')
ax2.set_title('Warp Result', fontsize=40)
x = [dst[0][0],dst[1][0],dst[2][0],dst[3][0],dst[0][0]]
y = [dst[0][1],dst[1][1],dst[2][1],dst[3][1],dst[0][1]]
ax2.plot(x, y, color='red', alpha=0.4, linewidth=3, solid_capstyle='round', zorder=2)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
##############################################################################
img = mpimg.imread('test_images/straight_lines1.jpg')
test_warper(img)
img = mpimg.imread('test_images/test5.jpg')
test_warper(img)
img = mpimg.imread('tmp-1.jpg')
test_warper(img)
4. Lane Dectection¶
In [7]:
#####################################################################
def pipeline(undist):
warped =warper(undist)
hls_binary = hls_threshold(warped, s_thresh=(5, 255), orient="x", sx_thresh=(20, 100))
return hls_binary
#####################################################################
def test_pipeline(img):
undist=img_undistort(img, mtx, dist)
result=pipeline(undist)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(result, cmap='gray')
ax2.set_title('Pipeline (White) & Histogram (Yellow)', fontsize=30)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
histogram = np.sum(result[result.shape[0]//2:,:], axis=0)
histogram = result.shape[0]-histogram
ax2.plot(histogram, color='yellow', linewidth=3)
###################################################################################
img = mpimg.imread('test_images/straight_lines1.jpg')
test_pipeline(img)
img = mpimg.imread('test_images/test5.jpg')
test_pipeline(img)
img = mpimg.imread('tmp-1.jpg')
test_pipeline(img)
In [8]:
def cal_leftright_coefs(binary_warped):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
# ///out_img = np.uint8(np.dstack((binary_warped, binary_warped, binary_warped))*255)
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
ww=histogram.shape[0]
leftx_base = np.argmax(histogram[50:midpoint])
rightx_base = np.argmax(histogram[midpoint:(ww-50)]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
#///cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
#///cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
if (len(leftx) > 1) :
left_fit = np.polyfit(lefty, leftx, 2)
else:
left_fit = (0,0,0)
if (len(rightx) > 1) :
right_fit = np.polyfit(righty, rightx, 2)
else:
right_fit = (0,0,0)
return leftx, lefty, rightx, righty, left_fit, right_fit
#####################################################################
def test_cal_leftright_coefs(img):
undist=img_undistort(img, mtx, dist)
binary_warped=pipeline(undist)
leftx, lefty, rightx, righty, left_fit, right_fit = cal_leftright_coefs(binary_warped)
# Plot the result
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
out_img=np.uint8(np.dstack((binary_warped, binary_warped, binary_warped))*255)
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
#####################################################################
def testA_cal_leftright_coefs(img):
undist=img_undistort(img, mtx, dist)
binary_warped=pipeline(undist)
margin = 100
leftx, lefty, rightx, righty, left_fit, right_fit = cal_leftright_coefs(binary_warped)
# Create an image to draw on and an image to show the selection window
out_img = np.uint8(np.dstack((binary_warped, binary_warped, binary_warped))*255)
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(result, cmap='gray')
ax2.set_title('Detected Lane', fontsize=30)
ax2.plot(left_fitx, ploty, color='yellow')
ax2.plot(right_fitx, ploty, color='yellow')
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
######################################################################################
img = mpimg.imread('test_images/straight_lines1.jpg')
testA_cal_leftright_coefs(img)
img = mpimg.imread('test_images/test5.jpg')
testA_cal_leftright_coefs(img)
img = mpimg.imread('tmp-1.jpg')
testA_cal_leftright_coefs(img)
5. Lane Curvature Determination¶
In [9]:
def cal_curveRad(img, leftx, lefty, rightx, righty, left_fit, right_fit):
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
curveRad = 0
offset = 0
if ((len(leftx)>1) & (len(rightx)>1)):
y_eval = np.max(lefty)
# Fit new polynomials to x,y in world space
if (len(leftx)>1):
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
else:
left_fit_cr = (0,0,0)
if (len(rightx)>1):
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
else:
right_fit_cr = (0,0,0)
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Now our radius of curvature is in meters
curveRad=0.5*(left_curverad+right_curverad)
#print("Curve Radius:", curveRad,"m")
#print(left_curverad, 'm', right_curverad, 'm')
car_center = img.shape[1]/2
ploty = img.shape[0]
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
lane_center = 0.5*(left_fitx + right_fitx )
offset = (car_center-lane_center) * xm_per_pix
#print("Offset:",offset, 'm')
return curveRad, offset
# Example values: 632.1 m 626.2 m
######################################################################################
#img = mpimg.imread('test_images/test2.jpg')
#cal_curveRad(img)
6. Warp the detected lane boundaries back onto the original image¶
In here, the lane boundaries and numerical estimation are displayed.
In [10]:
def draw_detectedLane(img):
undist=img_undistort(img, mtx, dist)
warped=pipeline(undist)
leftx, lefty, rightx, righty, left_fit, right_fit = cal_leftright_coefs(warped)
# Generate x and y values for plotting
ploty = np.linspace(0, warped.shape[0]-1, warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image to draw the lines on
warp_zero = np.zeros_like(warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (img.shape[1], img.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
curveRad, offset = cal_curveRad(result, leftx, lefty, rightx, righty, left_fit, right_fit)
new_img = np.copy(result)
font = cv2.FONT_HERSHEY_DUPLEX
text = 'Curve radius: ' + '{:04.2f}'.format(curveRad) + 'm'
cv2.putText(new_img, text, (40,70), font, 1.5, (200,255,155), 2, cv2.LINE_AA)
text = 'Offset: ' + '{:04.2f}'.format(offset) + 'm '
cv2.putText(new_img, text, (40,120), font, 1.5, (200,255,155), 2, cv2.LINE_AA)
#plt.imshow(new_img)
return new_img
#################################################################################################
def test_draw_detectedLane():
images = glob.glob('test_images/*.jpg')
mx=4
my=2
#fig, axes = plt.subplots(mx, my, sharex=True, sharey=True, figsize=(48, 18))
fig, axes = plt.subplots(mx, my, figsize=(30,30))
fig.tight_layout()
im=0
for fname in images:
img = mpimg.imread(fname)
new_img = draw_detectedLane(img)
axes[im%mx,im//mx].set_title(fname, fontsize=30)
axes[im%mx,im//mx].imshow(new_img)
im = im+1
test_draw_detectedLane()
In [11]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
def process_image(image):
#plt.imsave("tmp-3.jpg",image)
result = draw_detectedLane(image)
#plt.imshow(result)
return result
In [12]:
video_input = 'project_video.mp4'
video_output = 'project_video_output.mp4'
clip2 = VideoFileClip(video_input) #.subclip(23.5,23.5)
img_clip = clip2.fl_image(process_image)
%time img_clip.write_videofile(video_output, audio=False)
In [13]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(video_output))
Out[13]:
2. Challenge Video¶
In [14]:
video_input = 'challenge_video.mp4'
video_output = 'challenge_video_output.mp4'
clip2 = VideoFileClip(video_input) #.subclip(0,2)
img_clip = clip2.fl_image(process_image)
%time img_clip.write_videofile(video_output, audio=False)
In [15]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(video_output))
Out[15]:
3. Harder Challenge Video¶
In [16]:
video_input = 'harder_challenge_video.mp4'
video_output = 'harder_challenge_video_output.mp4'
clip2 = VideoFileClip(video_input) #.subclip(0,2)
img_clip = clip2.fl_image(process_image)
%time img_clip.write_videofile(video_output, audio=False)
In [17]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(video_output))
Out[17]:
Discussion¶
Noted that, the "Challenge Video" and "Harder Challenge Video" should be improved more in the further study.